Research Article
A network-based pharmacology study of active compounds and targets of Fritillaria thunbergii against influenza

https://doi.org/10.1016/j.compbiolchem.2020.107375Get rights and content

Highlights

  • Seasonal and pandemic influenza infections are serious threats to public health.

  • Computational research serves as a time-saving alternative to the experimental research that yields promising compounds and targets.

  • A network pharmacology-based strategy was used to predict potential compounds and target genes from Fritillaria thunbergii (FT) against influenza.

  • Compound-target (C-T), Compound-Disease Target (C-D), protein-protein interaction (PPI) and Compound-Disease Target-Pathway (C-D-P) networks were constructed to analyze FT’s effect against influenza.

  • Network analyses predicted two compounds (beta-sitosterol and pelargonidin) derived from FT that may be potent candidates for influenza treatment.

Abstract

Seasonal and pandemic influenza infections are serious threats to public health and the global economy. Since antigenic drift reduces the effectiveness of conventional therapies against the virus, herbal medicine has been proposed as an alternative. Fritillaria thunbergii (FT) have been traditionally used to treat airway inflammatory diseases such as coughs, bronchitis, pneumonia, and fever-based illnesses. Herein, we used a network pharmacology-based strategy to predict potential compounds from Fritillaria thunbergii (FT), target genes, and cellular pathways to better combat influenza and influenza-associated diseases. We identified five compounds, and 47 target genes using a compound-target network (C-T). Two compounds (beta-sitosterol and pelargonidin) and nine target genes (BCL2, CASP3, HSP90AA1, ICAM1, JUN, NOS2, PPARG, PTGS1, PTGS2) were identified using a compound-influenza disease target network (C-D). Protein-protein interaction (PPI) network was constructed and we identified eight proteins from nine target genes formed a network. The compound-disease-pathway network (C-D-P) revealed three classes of pathways linked to influenza: cancer, viral diseases, and inflammation. Taken together, our systems biology data from C-T, C-D, PPI and C-D-P networks predicted potent compounds from FT and new therapeutic targets and pathways involved in influenza.

Introduction

Every year, influenza infections have a major impact on public health across the globe. Annual influenza epidemics and recurring pandemics are serious threats to the global economy (Watanabe et al., 2010). The U.S. Food and Drug Administration (FDA) has approved two classes of drugs for treating influenza: M2 proton channel blockers (amantadine and rimantadine), and neuraminidase (NA) inhibitors (oseltamivir and zanamivir) (Yen, 2016). Although currently marketed antiviral drugs provide certain protection against infection, antigenic drift impairs the effectiveness of these therapies (Yen, 2016). Currently, most influenza viruses are resistant to M2 channel blockers, and the emergence of H1N1 strains of influenza resistant to NA inhibitors, such as oseltamivir, is becoming a serious concern in treating influenza (Hussain et al., 2017).

Traditional herbal medicine is a cost-effective system of medical practice that is raising interest as a substitute for chemical drugs in unresolved diseases (Wang et al., 2012). The bulbs from Fritillaria thunbergii (FT), a plant belonging to the lily family, have been traditionally used to treat airway inflammatory diseases such as coughs, bronchitis, pneumonia, and fever-based illnesses (Wang et al., 2011). In this study, we hypothesized that compounds from FT may also be effective in treating influenza and influenza-associated diseases.

Generally, herbal medicine contains numerous pharmacological compounds that may control complex diseases in a synergistic manner (Wang et al., 2012). However, only a part of them exhibit favorable pharmacokinetics with potential of a biological effect, thus there is a strong need to evaluate the complex physiological effects of herbal products (Li et al., 2012). Network pharmacology is an emerging computational approach for identifying novel compounds and therapeutic targets by systemically integrating pharmacokinetic data and biological actions in herbal medicine (Wang et al., 2013). There is a strong incentive to analyze underlying mechanisms in synergic effects of biologically active compounds isolated from herbs that complicate pharmacological research (Lee et al., 2018). Moreover,

computational research serves as a less time-consuming alternative to the experimental research that yields promising compounds and targets (Yu et al., 2012).

This study aimed to determine the absorption, distribution, metabolism, and excretion (ADME) pharmacokinetics data of therapeutic compounds from FT and their targets (C-T) using scientific strategies derived from network pharmacology. Identified compounds were further analyzed to determine whether they have common targets associated with influenza (C-D), and common targets were study through protein-protein interaction (PPI). We confirmed biological signaling pathways (C-D-P) activated by these compounds and targets. By doing so, we hoped to determine the potent-compound, targets, and mechanisms of action for FT derivatives against influenza infection.

Section snippets

Pharmacokinetic evaluation of compounds

All compounds were selected using the in silico ADME model with the Traditional Chinese Medicine Systems Pharmacology database (TCMSP). The ADME system used in this study predicts oral bioavailability (OB) and drug-likeness (DL). Compounds were retained only if OB ≥ 30% and DL ≥ 0.18 to satisfy the criteria suggested by the TCMSP database (ver. 2.3) (Ru et al., 2014).

Target genes linked to the compounds and construction of networks

Target genes linked to the identified compounds were further investigated using TCMSP, DrugBank (ver.5.1.4) and STITCH database

Selection of compounds using pharmacokinetic database

Seventeen compounds were retrieved using the TCMSP database (S1). Using the criteria of OB ≥ 30% and DL ≥ 0.18, the following seven compounds out of the original 17 compounds were screened: pelargonidin, beta-sitosterol, peimisine, zhebeiresinol, ziebeimine, 6-methoxyl-2-acetyl-3-methyl-1,4-naphthoquinone-8-O-beta-D-glucopyranoside, and chaksine (Table 1). These compounds were further analyzed to observe biological targets in ‘H. sapiens’.

Compound-Target network (C-T network)

Seven compounds identified from the pharmacokinetic

Discussion

Influenza is a complex, acute, respiratory infection that hinders the host immune system and causes inflammation. Due to antigenic drifts and the emergence of drug-resistant viruses, there is an urgent need to develop new therapeutic approaches to address the rise of drug resistance (Taubenberger and Morens, 2008). Generally, herbal medicine contains numerous pharmacological compounds that may control complex diseases in a synergistic manner. To identify potent compound, targets and underlying

Conclusion

Herein, we studied compounds derived from FT using a network pharmacology-based strategy to predict potential compounds, target genes, and pathways for treating influenza. From our network analysis, we identified two compounds, as well as nine target genes associated with influenza. Taken together, our C-T, C-D, PPI and C-D-P network analyses predicted potential FT-derived compounds and therapeutic targets against influenza associated inflammation.

CRediT authorship contribution statement

Minjee Kim: Methodology, Software, Data curation, Writing - original draft. Young Bong Kim: Conceptualization, Writing - review & editing.

Declaration of Competing Interest

The authors declare no conflict of interest.

Acknowledgements

This work was supported by Department of Biomedical Science and Engineering, Konkuk University.

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